Individual Leaf Identification from a Two-dimensional Monocotyledon Image Based on Phytomorphological Graph Reconstruction

Abstract

We aim to automatically identify all individual leaves in a monocotyledon image. Leaf identification is one of key technologies to acquire plant phenotypes such as a leaf length, a leaf count, and a growth rate. However, it is challenging to identify individual leaves from a monocotyledonous plant image due to their complicated occlusion and similar colors. We adopt a graph-theoretical approach to overcome the leaf occlusion and the color similarity between leaves, and apply a combinatorial optimization technique to simultaneously identify all individual leaves based on local and global characteristics of plants. We propose a plant-wide global optimization to identify all true leaves in a plant, which is formulated as a minimum path cover problem that we call the phytomorphological graph reconstruction. A phytomorphological graph of a plant image is a graph reflecting the plant's structure and is constructed through a plant region extraction and a skeletonization. All candidate leaf paths in the graph are extracted and their leaf path likelihoods are computed from the graph, where the leaf path likelihood means a measure of similarity with true leaves. The candidate leaf paths and their path likelihoods are used in the graph reconstruction process in order to find an optimal subset of the leaf paths which are similar to true leaves and can restore the phytomorphological graph as completely as possible. Experiments show that our proposed system effectively identifies individual leaves from a single 2-dimensional image of rice plants, which enables us to take an accurate and efficient high-throughput measurement of phenotypes for plants.